code: def restore_train_state(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model: Any,
      dataset: dataset_utils.Dataset,
  ):
      """Initializes the model state."""
      # Initialize model.
      rng, init_rng = jax.random.split(rng)
      (params, model_state, _, _) = train_utils.initialize_model(
          model_def=model.flax_model,
          input_spec=[
              (
                  dataset.meta_data["input_shape"],
                  dataset.meta_data.get("input_dtype", jnp.float32),
              )
          ],
          config=config,
          rngs=init_rng,
      )
      # Create optimizer.
      optimizer_config = optimizers.get_optax_optimizer_config(config)
      # If the config is already an optax-compatible config, better call directly:
      #   optimizers.get_optimizer(config.optimizer_configs, lr_fn)
      tx = optimizers.get_optimizer(optimizer_config, 0, params=params)
      # We jit this, such that the arrays that are created on the same device as the
      # input is, in this case the CPU. Else they'd be on device[0].
      opt_state = jax.jit(tx.init, backend="cpu")(params)
  
      rng, train_rng = jax.random.split(rng)
      train_state = train_utils.TrainState(
          global_step=0,
          opt_state=opt_state,
          tx=tx,
          params=params,
          model_state=model_state,
          rng=train_rng,
          metadata={},
      )
      init_checkpoint_path = config.init_from.get("checkpoint_path")
      restored_train_state = (
          pretrain_utils.restore_pretrained_checkpoint(
              init_checkpoint_path, train_state, assert_exist=True
          )
      )
      current_step = restored_train_state.global_step
      logging.info(
          "Parameter summary after initialising from restored train state "
          "at step %d:",
          current_step,
      )
      debug_utils.log_param_shapes(restored_train_state.params)
      return restored_train_state, current_step
  
imports: import datetime
  import functools
  import os
  import pickle
  from typing import Any, Callable, Optional, Type
  from absl import logging
  from clu import metric_writers
  from flax import jax_utils
  import flax.linen
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.svvit import classification_trainer
  from scenic.projects.svvit import metrics
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: restore_train_state(rng, config, model, dataset) and returns: restored_train_state,  current_step